Security cameras detect motion and start recording a video or capture pictures of an object. Often times, if the moving object is a person, the face of the person is only partially captured. Face recognition technology will not be able to identify the person with only a partial capture. Face recognition is far from perfect and struggles to perform under some conditions. Face recognition can be successful when full frontal faces or when the face is turned 20 degrees. The closer the captured face is to a profile view, the more facial recognition is likely to be unsuccessful. Other conditions where face recognition does not work well include poor lighting, sunglasses, hats, scarves, beards, long hair, makeup or other objects partially covering the face, and low resolution images. Another serious disadvantage is that facial recognition is less effective if facial expressions vary.
The main disadvantage of the current face recognition systems is in not getting a frontal face image for recognition. Implementing multiple cameras at different angles at the same location to increase the chance of capturing good face images may not be possible or effective. Furthermore, providing video data that does not capture a full frontal face image reduces an ability to perform real-time facial recognition. Without knowing which frames are likely to have full frontal face images can waste processing resources and extend the time needed to perform facial recognition. To perform real-time analysis of face recognition, the number of images must be small enough for real-time analysis (i.e., approximately 10 ms) and contain good frontal face images.
It would be desirable to implement face recognition systems with external stimulus.